The invention relates to a method for matching grayvalued first elements of a first data set with grayvalued second elements of a second data set so that a first distribution of the first elements optimally matches a second distribution of the second elements, as being furthermore recited in the preamble of claim 1.
Various prior methods have been applied for matching such 2D- or 3D-grayvalue distributions. The elements may be pixels in a spatial 2D or 3D configuration, voxels in 3D, or other. The grayvalue is used for short for a respective single-valued parameter of each such element, such as the grayness of a spatial pixel, but other such variables have been used. A well-known field of application is medical graphical data processing. Such methods examine a plurality of alternative relative locations and orientations, through techniques like correlation or pixel similarity. The actual matching will be decided as based on the highest response for the criterion applied. Next, a transformation matrix will describe the transform from one distribution to the other for the matching position and orientation.
Generally, transformations in medical applications have been limited to rigid or affine transforms, sometimes in a combination wherein selected bone parts of the distribution undergo rigid transformations only, whilst surrounding tissue is transformed by an affine transform.
Another method uses active 2D-contours or active 3D-objects, that may be used to detect edges or sides of anatomical objects in scanned body data. Such contours/objects are in fact virtual objects, that allow finding features in the image data that represent edges of anatomical objects. Such technique will actively find translations, rotations, and deformations of the objects, which will then make them better fit, and which technique may be used to detect edges or sides of anatomical objects in scanned body data. Such techniques will actively find translations, rotations, and deformations of objects, to make them better fit to features in the image data. The best match is one wherein the object has moved around to a stable position that gives the strongest response. Such can either be a local best fit or rather, a global one.
The present inventor has recognized that current procedures are computationally expensive, because the choosing of the best matching transform needs inspecting multiple locations and orientations, and each thereof requires calculating the matching criterion. Moreover, the present inventor has recognized the benefit of iterating procedures.
On the other hand, current methods based on active contours or objects base on interpreting local image features, and in particular, “thin” features. In 2D, such could be a one-dimensional contour or centerline, and in 3D, a curved 2D surface. These methods are therefore typically edge-based, and grayvalue distributions inside a region or object are left out of consideration. On the other hand, a non-uniform grayvalue distribution inside could suggest a false edge, thereby positioning the active contour or object at an erroneous location.